Early detection of buzzwords based on large-scale time-series analysis of blog entries

HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media Pub Date : 2012-06-25 DOI:10.1145/2309996.2310042

Shinsuke Nakajima, Jianwei Zhang, Y. Inagaki, Reyn Y. Nakamoto

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引用次数: 12

Abstract

In this paper, we discuss a method for early detection of "gradual buzzwords" by analyzing time-series data of blog entries. We observe the process in which certain topics grow to become major buzzwords and determine the key indicators that are necessary for their early detection. From the analysis results based on 81,922,977 blog entries from 3,776,154 blog websites posted in the past two years, we find that as topics grow to become major buzzwords, the percentages of blog entries from the blogger communities closely related to the target buzzword decrease gradually, and the percentages of blog entries from the weakly related blogger communities increase gradually. We then describe a method for early detection of these buzzwords, which is dependent on identifying the blogger communities which are closely related to these buzzwords. Moreover, we verify the effectiveness of the proposed method through experimentation that compares the rankings of several buzzword candidates with a real-life idol group popularity competition.

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基于大规模博客时间序列分析的热词早期检测

本文通过分析博客条目的时间序列数据，探讨了一种“渐进式流行语”的早期检测方法。我们观察某些话题成长为主要流行语的过程，并确定早期发现这些话题所需的关键指标。通过对3776154个博客网站近两年发布的81922977篇博客文章的分析结果发现，随着话题成为主要流行语，与目标流行语密切相关的博客社区的博客文章所占比例逐渐下降，而与目标流行语关联度较弱的博客社区的博客文章所占比例逐渐上升。然后，我们描述了一种早期检测这些流行语的方法，该方法依赖于识别与这些流行语密切相关的博客社区。此外，我们通过实验验证了所提出方法的有效性，该实验将几个流行语候选人的排名与现实生活中的偶像团体人气竞争进行比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media

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